Discovering knowledge from medical databases using evolutionary algorithms   [MD] [EA]

by

Wong, M., L., Lam, W., Leung, K., S., Ngan, P., S. and Cheng, J., C., Y.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: IEEE Engineering in Medicine and Biology Magazine (Journal), 2000, p. 45-55
Keywords:genetic algorithms, genetic programming, database management systems, medical databases, knowledge discovery, Bayesian networks, causality relationship models, Bayesian network learning process, continuous variables, advanced evolutionary algorithms, evolutionary programming, learning tasks, fracture database, child fractures, scoliosis database, scoliosis classification, novel clinical knowledge, database errors
Abstract:
Discusses learning roles and causal structures for capturing patterns and causality relationships. The authors present their approach for knowledge discovery from [KD] two specific medical databases. [MD] First, rules are learned to represent the interesting patterns of the data. Second, Bayesian networks [BN] are induced to act as causality relationship models [CRM] among the attributes. The Bayesian network learning process [BN] is divided into two phases. In the first phase, a discretization policy is learned to discretize the continuous variables, [CV] and then Bayesian network structures [BN] are induced in the second phase. The authors employ advanced evolutionary algorithms [AEA] [EA] such as generic genetic programming, evolutionary programming, [GP] [EP] and genetic algorithms to [GA] conduct the learning tasks. From [LT] the fracture database, [FD] they discovered knowledge about the patterns of child fractures. From [CF] the scoliosis database, [SD] they discovered knowledge about the classification of scoliosis. They also found unexpected rules that led to discovery of errors in the database. These results demonstrate that the knowledge discovery process [KD] can find interesting knowledge about the data, which can provide novel clinical knowledge [NCK] as well as suggest refinements of the existing knowledge.
URL(s):PDF

Review item:

Mark as doublet (will be reviewed)

Print entry




BibTex:
@Article{wong:2000:dkm,
  author =       "Man Leung Wong and Wai Lam and Kwong Sak Leung and Po
                 Shun Ngan and Jack C. Y. Cheng",
  title =        "Discovering knowledge from medical databases using
                 evolutionary algorithms",
  journal =      "IEEE Engineering in Medicine and Biology Magazine",
  year =         "2000",
  volume =       "19",
  number =       "4",
  pages =        "45--55",
  month =        jul # "-" # aug,
  keywords =     "genetic algorithms, genetic programming, database
                 management systems, medical databases, knowledge
                 discovery, Bayesian networks, causality relationship
                 models, Bayesian network learning process, continuous
                 variables, advanced evolutionary algorithms,
                 evolutionary programming, learning tasks, fracture
                 database, child fractures, scoliosis database,
                 scoliosis classification, novel clinical knowledge,
                 database errors",
  ISSN =         "0739-5175",
  URL =          "http://ieeexplore.ieee.org/iel5/51/18543/00853481.pdf",
  size =         "11 pages",
  abstract =     "Discusses learning roles and causal structures for
                 capturing patterns and causality relationships. The
                 authors present their approach for knowledge discovery
                 from two specific medical databases. First, rules are
                 learned to represent the interesting patterns of the
                 data. Second, Bayesian networks are induced to act as
                 causality relationship models among the attributes. The
                 Bayesian network learning process is divided into two
                 phases. In the first phase, a discretization policy is
                 learned to discretize the continuous variables, and
                 then Bayesian network structures are induced in the
                 second phase. The authors employ advanced evolutionary
                 algorithms such as generic genetic programming,
                 evolutionary programming, and genetic algorithms to
                 conduct the learning tasks. From the fracture database,
                 they discovered knowledge about the patterns of child
                 fractures. From the scoliosis database, they discovered
                 knowledge about the classification of scoliosis. They
                 also found unexpected rules that led to discovery of
                 errors in the database. These results demonstrate that
                 the knowledge discovery process can find interesting
                 knowledge about the data, which can provide novel
                 clinical knowledge as well as suggest refinements of
                 the existing knowledge.",
}